The proposed research addresses the problem of early detection of bioterrorism attacks where bioterrorist release a biological agent into the atmosphere as an aerosol. Such attacks have the potential to kill tens to hundreds of thousands of individuals. Early detection of a windborne attack enables an early response and the earlier the response, the greater the number of lives saved. Over the past three years, we have developed an algorithm designed specifically to detect outbreaks caused by windborne attacks. The potential advantages of this algorithm include: 1. Earlier detection at a lower false-alarm rate as a result of utilizing weather data and knowledge of the spatial and temporal patterns in case distributions in an outbreak resulting from an aerosol release. 2. Partial characterization of the outbreak at the time of detection as an outdoor, aerosol release of a biological agent. 3. A best estimate of location, quantity, and timing of the release of the biological agent. Once responders are aware of an event, this information is essential for the management of additional surveillance and response. We propose to further develop the algorithm and measure the degree to which we can achieve these benefits.The specific aims of the research are to: 1. Determine the ability of the Bayesian Aerosol Release Detector (BARD) to discriminate between outbreaks caused by windborne transmission of biological agents and outbreaks due to other modes of transmission. 2. Compare the performance of BARD for the detection of simulated windborne outbreaks with the performance of algorithms not specifically designed to detect windborne outbreaks. 3. Determine whether inference with BARD remains computationally tractable after extending in a number of ways its models of windborne outbreaks. 4. Evaluate the extensions made in Specific Aim #3 for improvements in detection performance relative to the baseline versions of BARD used in Specific Aims #1 and #2.